Download Basic Marketing, 16e - Cal State LA

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Embodied cognitive science wikipedia, lookup

Gene expression programming wikipedia, lookup

Ethics of artificial intelligence wikipedia, lookup

Collaborative information seeking wikipedia, lookup

Genetic algorithm wikipedia, lookup

Philosophy of artificial intelligence wikipedia, lookup

Existential risk from artificial general intelligence wikipedia, lookup

Intelligence explosion wikipedia, lookup

History of artificial intelligence wikipedia, lookup

Machine learning wikipedia, lookup

Knowledge representation and reasoning wikipedia, lookup

AI winter wikipedia, lookup

Incomplete Nature wikipedia, lookup

Fuzzy logic wikipedia, lookup

Agent (The Matrix) wikipedia, lookup

Agent-based model wikipedia, lookup

Agent-based model in biology wikipedia, lookup

Transcript
Chapter 4
Decision Support and Artificial
Intelligence: Brainpower for Your
Business
McGraw-Hill/Irwin
Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.
STUDENT LEARNING OUTCOMES
1.
2.
3.
Compare and contrast decision support
systems and geographic information
systems.
Define expert systems and describe the
types of problem to which they are
applicable.
Define neural networks and fuzzy logic and
the use of these AI tools.
4-2
STUDENT LEARNING OUTCOMES
4.
5.
Define genetic algorithms and list the
concepts on which they are based and the
types of problems they solve.
Describe the four types of agent-based
technologies.
4-3
AN NFL TEAM NEEDS MORE
THAN ATHLETIC ABILITY
 The
Patriots football team is a very
successful one
 The team uses a decision support system to
analyze the opposition’s game
 The software breaks down the game day
video into plays and player actions
 With this information the Patriots can better
formulate their strategy
4-4
AN NFL TEAM NEEDS MORE
THAN ATHLETIC ABILITY
1.
2.
3.
DSS with predictive analytics used to gain
the advantage in other sports? Choose a
sport and explain how that might work.
Would allowing coaches to have laptops on
the field change the game appreciably?
What other aspect of football could be
improved by decision support systems?
4-5
INTRODUCTION

Phases of decision making
1.
Intelligence
2.
Design
3.
Choice
4.
Implementation
4-6
Four Phases of Decision Making
4-7
Types of Decisions You Face
 Structured
decision
 Nonstructured
 Recurring
decision
decision
 Nonrecurring
(ad hoc) decision
4-8
Types of Decisions You Face
EASIEST
MOST
DIFFICULT
4-9
CHAPTER ORGANIZATION
1.
Decision Support Systems

2.
Geographic Information Systems

3.
Learning outcome #1
Expert Systems

4.
Learning outcome #1
Learning outcome #2
Neural Networks and Fuzzy Logic

Learning outcome #3
4-10
CHAPTER ORGANIZATION
5.
Genetic Algorithms

6.
Learning outcome #4
Intelligent Agents

Learning outcome #5
4-11
DECISION SUPPORT SYSTEMS
 Decision
support system (DSS)
4-12
Alliance between You and a DSS
4-13
Components of a DSS
 Model
management component
 Data
management component
 User
interface management component
4-14
Components of a DSS
4-15
Predictive Analytics
 Analytics
(predictive analytics)
4-16
GEOGRAPHIC INFORMATION
SYSTEMS
 Geographic
information system (GIS)
4-17
Zillow GIS Software for Denver
4-18
ARTIFICIAL INTELLIGENCE
 DSSs
and GISs support decision making; you
are still completely in charge
 Artificial intelligence
4-19
EXPERT SYSTEMS
 Expert
(knowledge-based) system
4-20
Traffic Light Expert System
4-21
What Expert Systems Can and
Can’t Do
 An
expert system can
 An
expert system can’t

4-22
NEURAL NETWORKS AND FUZZY
LOGIC
 Neural
network (artificial neural network or
ANN)

4-23
Neural Networks Can…

4-24
Fuzzy Logic
 Fuzzy
logic
4-25
GENETIC ALGORITHMS
 Genetic
algorithm
4-26
Evolutionary Principles of
Genetic Algorithms
1.
Selection
2.
Crossover
3.
Mutation
4.
4-27
Genetic Algorithms Can…
4-28
INTELLIGENT AGENTS
 Intelligent
agent
 Types
4-29
Information Agents
 Information
 Ex:
Agents
Buyer agent or shopping bot
4-30
Monitoring-and-Surveillance
Agents

Monitoring-and-surveillance (predictive)
agents
4-31
Data-Mining Agents

Data-mining agent
4-32
User Agents
 User
or personal agent
 Examples:
4-33
MULTI-AGENT SYSTEMS AND
AGENT-BASED MODELING

Biomimicry
4-34
Agent-Based Modeling
 Agent-based
 Multi-agent
modeling
system
4-35
Business Applications
Airlines – cargo routing
 P&G – supply network optimization
 Air Liquide America – reduce production and
distribution costs
 Merck – distributing anti-AIDS drugs in Africa
 Ford – balance production costs & consumer
demands
 Edison Chouest – deploy service and supply
vessels
 Southwest
4-36
Swarm Intelligence

Swarm (collective) intelligence
4-37
Characteristics of Swarm
Intelligence
– adaptable to change
 Robustness – tasks are completed even if
some individuals are removed
 Decentralization – each individual has a
simple job to do
 Flexibility
4-38